Toward adaptive radiotherapy for head and neck patients: Feasibility study on using CT-to-CBCT deformable registration for "dose of the day" calculations

Evaluation of dose delivery based on deformed CT using a commercial software for lung cancer

This study employed a commercial software velocity to perform deformable registration and dose calculation on deformed CT images, aiming to assess the accuracy of dose delivery during the radiotherapy for lung cancers. A total of 20 patients with lung cancer were enrolled in this study. Adaptive CT (ACT) was generated by deformed the planning CT (pCT) to the CBCT of initial radiotherapy fraction, followed by contour propagation and dose recalculation. There was not significant difference between volumes of GTV and CTV calculated from the ACT and pCT. However, significant differences in dice similarity coefficient (DSC) and coverage ratio (CR) between GTV and CTV were observed, with lower values for GTV volumes below 15 cc. The mean differences in dose corresponding to 95% of the GTV, GTV-P, CTV, and CTV-P between ACT and pCT were - 0.32%, 4.52%, 2.17%, and 4.71%, respectively. For the dose corresponding to 99%, the discrepancies were - 0.18%, 8.35%, 1.92%, and 24.96%, respectively. These differences in dose primarily appeared at the edges of the target areas. Notably, a significant enhancement of dose corresponding to 1 cc for spinal cord was observed in ACT, compared with pCT. There was no statistical difference in the mean dose of lungs and heart. In general, for lung cancer patients, anatomical motion may result in both CTV and GTV moving outside the original irradiation region. The dose difference within the original target area was small, but the difference in the planning target area was considerable.

Structure-specific rigid dose accumulation dosimetric analysis of ablative stereotactic MRI-guided adaptive radiation therapy in ultracentral lung lesions

BACKGROUND

Definitive local therapy with stereotactic ablative radiation therapy (SABR) for ultracentral lung lesions is associated with a high risk of toxicity, including treatment related death. Stereotactic MR-guided adaptive radiation therapy (SMART) can overcome many of the challenges associated with SABR treatment of ultracentral lesions.

METHODS

We retrospectively identified 14 consecutive patients who received SMART to ultracentral lung lesions from 10/2019 to 01/2021. Patients had a median distance from the proximal bronchial tree (PBT) of 0.38 cm. Tumors were most often lung primary (64.3%) and HILUS group A (85.7%). A structure-specific rigid registration approach was used for cumulative dose analysis. Kaplan-Meier log-rank analysis was used for clinical outcome data and the Wilcoxon Signed Rank test was used for dosimetric data.

RESULTS

Here we show that SMART dosimetric improvements in favor of delivered plans over predicted non-adapted plans for PBT, with improvements in proximal bronchial tree DMax of 5.7 Gy (p = 0.002) and gross tumor 100% prescription coverage of 7.3% (p = 0.002). The mean estimated follow-up is 17.2 months and 2-year local control and local failure free survival rates are 92.9% and 85.7%, respectively. There are no grade ≥ 3 toxicities.

CONCLUSIONS

SMART has dosimetric advantages and excellent clinical outcomes for ultracentral lung tumors. Daily plan adaptation reliably improves target coverage while simultaneously reducing doses to the proximal airways. These results further characterize the therapeutic window improvements for SMART. Structure-specific rigid dose accumulation dosimetric analysis provides insights that elucidate the dosimetric advantages of SMART more so than per fractional analysis alone.

ACR benchmark testing of a novel high-speed ring-gantry linac kV-CBCT system

A new generation cone-beam computed tomography (CBCT) system with new hardware design and advanced image reconstruction algorithms is available for radiation treatment simulation or adaptive radiotherapy (HyperSight CBCT imaging solution, Varian Medical Systems-a Siemens Healthineers company). This study assesses the CBCT image quality metrics using the criteria routinely used for diagnostic CT scanner accreditation as a first step towards the future use of HyperSight CBCT images for treatment planning and target/organ delineations. Image performance was evaluated using American College of Radiology (ACR) Program accreditation phantom tests for diagnostic computed tomography systems (CTs) and compared HyperSight images with a standard treatment planning diagnostic CT scanner (Siemens SOMATOM Edge) and with existing CBCT systems (Varian TrueBeam version 2.7 and Varian Halcyon version 2.0).  Image quality performance for all Varian HyperSight CBCT vendor-provided imaging protocols were assessed using ACR head and body ring CT phantoms, then compared to existing imaging modalities. Image quality analysis metrics included contrast-to-noise (CNR), spatial resolution, Hounsfield number (HU) accuracy, image scaling, and uniformity. All image quality assessments were made following the recommendations and passing criteria provided by the ACR. The Varian HyperSight CBCT imaging system demonstrated excellent image quality, with the majority of vendor-provided imaging protocols capable of passing all ACR CT accreditation standards. Nearly all (8/11) vendor-provided protocols passed ACR criteria using the ACR head phantom, with the Abdomen Large, Pelvis Large, and H&N vendor-provided protocols produced HU uniformity values slightly exceeding passing criteria but remained within the allowable minor deviation levels (5-7 HU maximum differences). Compared to other existing CT and CBCT imaging modalities, both HyperSight Head and Pelvis imaging protocols matched the performance of the SOMATOM CT scanner, and both the HyperSight and SOMATOM CT substantially surpassed the performance of the Halcyon 2.0 and TrueBeam version 2.7 systems. Varian HyperSight CBCT imaging system could pass almost all tests for all vendor-provided protocols using ACR accreditation criteria, with image quality similar to those produced by diagnostic CT scanners and significantly better than existing linac-based CBCT imaging systems.

Clinical Super-Resolution Computed Tomography of Bone Microstructure: Application in Musculoskeletal and Dental Imaging

PURPOSE

Clinical cone-beam computed tomography (CBCT) devices are limited to imaging features of half a millimeter in size and cannot quantify the tissue microstructure. We demonstrate a robust deep-learning method for enhancing clinical CT images, only requiring a limited set of easy-to-acquire training data.

METHODS

Knee tissue from five cadavers and six total knee replacement patients, and 14 teeth from eight patients were scanned using laboratory CT as training data for the developed super-resolution (SR) technique. The method was benchmarked against ex vivo test set, 52 osteochondral samples are imaged with clinical and laboratory CT. A quality assurance phantom was imaged with clinical CT to quantify the technical image quality. To visually assess the clinical image quality, musculoskeletal and maxillofacial CBCT studies were enhanced with SR and contrasted to interpolated images. A dental radiologist and surgeon reviewed the maxillofacial images.

RESULTS

The SR models predicted the bone morphological parameters on the ex vivo test set more accurately than conventional image processing. The phantom analysis confirmed higher spatial resolution on the SR images than interpolation, but image grayscales were modified. Musculoskeletal and maxillofacial CBCT images showed more details on SR than interpolation; however, artifacts were observed near the crown of the teeth. The readers assessed mediocre overall scores for both SR and interpolation. The source code and pretrained networks are publicly available.

CONCLUSION

Model training with laboratory modalities could push the resolution limit beyond state-of-the-art clinical musculoskeletal and dental CBCT. A larger maxillofacial training dataset is recommended for dental applications.

Uncertain prediction of deformable image registration on lung CT using multi-category features and supervised learning

The assessment of deformable registration uncertainty is an important task for the safety and reliability of registration methods in clinical applications. However, it is typically done by a manual and time-consuming procedure. We propose a novel automatic method to predict registration uncertainty based on multi-category features and supervised learning. Three types of features, including deformation field statistical features, deformation field physiologically realistic features, and image similarity features, are introduced and calculated to train the random forest regressor for local registration uncertain prediction. Deformation field statistical features represent the numerical stability of registration optimization, which are correlated to the uncertainty of deformation fields; deformation field physiologically realistic features represent the biomechanical properties of organ motions, which mathematically reflect the physiological reality of deformation; image similarity features reflect the similarity between the warped image and fixed image. The multi-category features comprehensively reflect the registration uncertainty. The strategy of spatial adaptive random perturbations is also introduced to accurately simulate spatial distribution of registration uncertainty, which makes deformation field statistical features more discriminative to the uncertainty of deformation fields. Experiments were conducted on three publicly available thoracic CT image datasets. Seventeen randomly selected image pairs are used to train the random forest model, and 9 image pairs are used to evaluate the prediction model. The quantitative experiments on lung CT images show that the proposed method outperforms the baseline method for uncertain prediction of classical iterative optimization-based registration and deep learning-based registration with different registration qualities. The proposed method achieves good performance for registration uncertain prediction, which has great potential in improving the accuracy of registration uncertain prediction.

Improving hybrid image and structure-based deformable image registration for large internal deformations

Objective.Deformable image registration (DIR) is a widely used technique in radiotherapy. Complex deformations, resulting from large anatomical changes, are a regular challenge. DIR algorithms generally seek a balance between capturing large deformations and preserving a smooth deformation vector field (DVF). We propose a novel structure-based term that can enhance the registration efficacy while ensuring a smooth DVF.Approach.The proposed novel similarity metric for controlling structures was introduced as a new term into a commercially available algorithm. Its performance was compared to the original algorithm using a dataset of 46 patients who received pelvic re-irradiation, many of which exhibited complex deformations.Main results.The mean Dice Similarity Coefficient (DSC) under the improved algorithm was 0.96, 0.94, 0.76, and 0.91 for bladder, rectum, colon, and bone respectively, compared to 0.69, 0.89, 0.62, and 0.88 for the original algorithm. The improvement was more pronounced for complex deformations.Significance.With this work, we have demonstrated that the proposed term is able to improve registration accuracy for complex cases while maintaining realistic deformations.

Prediction of SBRT response in liver cancer by combining original and delta cone-beam CT radiomics: a pilot study

This study aims to explore the feasibility of utilizing a combination of original and delta cone-beam CT (CBCT) radiomics for predicting treatment response in liver tumors undergoing stereotactic body radiation therapy (SBRT). A total of 49 patients are included in this study, with 36 receiving 5-fraction SBRT, 3 receiving 4-fraction SBRT, and 10 receiving 3-fraction SBRT. The CBCT and planning CT images from liver cancer patients who underwent SBRT are collected to extract overall 547 radiomics features. The CBCT features which are reproducible and interchangeable with pCT are selected for modeling analysis. The delta features between fractions are calculated to depict tumor change. The patients with 4-fraction SBRT are only used for screening robust features. In patients receiving 5-fraction SBRT, the predictive ability of both original and delta CBCT features for two-level treatment response (local efficacy vs. local non-efficacy; complete response (CR) vs. partial response (PR)) is assessed by utilizing multivariable logistic regression with leave-one-out cross-validation. Additionally, univariate analysis is conducted to validate the capability of CBCT features in identifying local efficacy in patients receiving 3-fraction SBRT. In patients receiving 5-fraction SBRT, the combined models incorporating original and delta CBCT radiomics features demonstrate higher area under the curve (AUC) values compared to models using either original or delta features alone for both classification tasks. The AUC values for predicting local efficacy vs. local non-efficacy are 0.58 for original features, 0.82 for delta features, and 0.90 for combined features. For distinguishing PR from CR, the respective AUC values for original, delta and combined features are 0.79, 0.80, and 0.89. In patients receiving 3-fraction SBRT, eight valuable CBCT radiomics features are identified for predicting local efficacy. The combination of original and delta radiomics derived from fractionated CBCT images in liver cancer patients undergoing SBRT shows promise in providing comprehensive information for predicting treatment response.

Feasibility of Ethos adaptive treatments of lung tumors and associated quality assurance

MOTIVATION

Online adaptive radiotherapy with Ethos is based on the anatomy determined from daily cone beam computed tomography (CBCT) images. Dose optimization and computation are performed on the density map of a synthetic CT (sCT), a deformable registration of the initial planning CT (pCT) onto the current CBCT. Large density changes as present in the lung region are challenging the system.

METHODS

Treatment plans for Ethos were created and delivered for 1, 2, and 3 cm diameter lung lesions in an anthropomorphic phantom, combining different insets in the pCT and during adaptive and non-adaptive treatment sessions. Primary and secondary dose calculations as well as back-projected dose from portal images were evaluated.

RESULTS

Density changes due to changed insets were not considered in the sCTs. This resulted in errors in the dose; for example, -15.9% of the mean dose for a plan when changing from a 3 cm inset in the pCT to 1 cm at the time of treatment. Secondary dose calculation is based on the sCT and could therefore not reveal these dose errors. However, dose calculation on the CBCT, either as a recalculation in the treatment planning system or as pre-treatment quality assurance (QA) before the treatment, indicated the differences. EPID in-vivo QA also reported discrepancies between calculated and delivered dose distributions.

CONCLUSIONS

An incorrect density distribution in the sCT has an impact on the dose calculation accuracy in the adaptive treatment workflow with the Ethos system. Additional quality checks of the sCT can detect such errors.

Functional information guided adaptive radiation therapy

Introduction

Functional informaton is introduced as the mechanism to adapt cancer therapies uniquely to individual patients based on changes defined by qualified tumor biomarkers.

Methods

To demonstrate the methodology, a tumor volume biomarker model, characterized by a tumor volume reduction rate coefficient, is used to adapt a tumor cell survival bioresponse radiotherapy model in terms of therapeutic radiation dose. Tumor volume, acquired from imaging data, serves as a surrogate measurement for tumor cell death, but the biomarker model derived from this data cannot be used to calculate the radiation dose absorbed by the target tumor. However, functional information does provide a mathematical connection between the tumor volume biomarker model and the tumor cell survival bioresponse model by quantifying both data sets in the units of information, thus creating an analytic conduit from bioresponse to biomarker.

Results

The information guided process for individualized dose adaptations using information values acquired from the tumor cell survival bioresponse model and the tumor volume biomarker model are presented in detailed form by flowchart and tabular data. Clinical data are used to generate a presentation that assists investigator application of the information guided methodology to adaptive cancer therapy research.

Conclusions

Information guided adaptation of bioresponse using surrogate data is extensible across multiple research fields because functional information mathematically connects disparate bioresponse and biomarker data sets. Thus, functional information offers adaptive cancer therapy by mathematically connecting immunotherapy, chemotherapy, and radiotherapy cancer treatment processes to implement individualized treatment plans.

Investigation of autosegmentation techniques on T2-weighted MRI for off-line dose reconstruction in MR-linac workflow for head and neck cancers

BACKGROUND

In order to accurately accumulate delivered dose for head and neck cancer patients treated with the Adapt to Position workflow on the 1.5T magnetic resonance imaging (MRI)-linear accelerator (MR-linac), the low-resolution T2-weighted MRIs used for daily setup must be segmented to enable reconstruction of the delivered dose at each fraction.

PURPOSE

In this pilot study, we evaluate various autosegmentation methods for head and neck organs at risk (OARs) on on-board setup MRIs from the MR-linac for off-line reconstruction of delivered dose.

METHODS

Seven OARs (parotid glands, submandibular glands, mandible, spinal cord, and brainstem) were contoured on 43 images by seven observers each. Ground truth contours were generated using a simultaneous truth and performance level estimation (STAPLE) algorithm. Twenty total autosegmentation methods were evaluated in ADMIRE: 1-9) atlas-based autosegmentation using a population atlas library (PAL) of 5/10/15 patients with STAPLE, patch fusion (PF), random forest (RF) for label fusion; 10-19) autosegmentation using images from a patient's 1-4 prior fractions (individualized patient prior [IPP]) using STAPLE/PF/RF; 20) deep learning (DL) (3D ResUNet trained on 43 ground truth structure sets plus 45 contoured by one observer). Execution time was measured for each method. Autosegmented structures were compared to ground truth structures using the Dice similarity coefficient, mean surface distance (MSD), Hausdorff distance (HD), and Jaccard index (JI). For each metric and OAR, performance was compared to the inter-observer variability using Dunn's test with control. Methods were compared pairwise using the Steel-Dwass test for each metric pooled across all OARs. Further dosimetric analysis was performed on three high-performing autosegmentation methods (DL, IPP with RF and 4 fractions [IPP_RF_4], IPP with 1 fraction [IPP_1]), and one low-performing (PAL with STAPLE and 5 atlases [PAL_ST_5]). For five patients, delivered doses from clinical plans were recalculated on setup images with ground truth and autosegmented structure sets. Differences in maximum and mean dose to each structure between the ground truth and autosegmented structures were calculated and correlated with geometric metrics.

RESULTS

DL and IPP methods performed best overall, all significantly outperforming inter-observer variability and with no significant difference between methods in pairwise comparison. PAL methods performed worst overall; most were not significantly different from the inter-observer variability or from each other. DL was the fastest method (33 s per case) and PAL methods the slowest (3.7-13.8 min per case). Execution time increased with a number of prior fractions/atlases for IPP and PAL. For DL, IPP_1, and IPP_RF_4, the majority (95%) of dose differences were within ± 250 cGy from ground truth, but outlier differences up to 785 cGy occurred. Dose differences were much higher for PAL_ST_5, with outlier differences up to 1920 cGy. Dose differences showed weak but significant correlations with all geometric metrics (R2 between 0.030 and 0.314).

CONCLUSIONS

The autosegmentation methods offering the best combination of performance and execution time are DL and IPP_1. Dose reconstruction on on-board T2-weighted MRIs is feasible with autosegmented structures with minimal dosimetric variation from ground truth, but contours should be visually inspected prior to dose reconstruction in an end-to-end dose accumulation workflow.

Review and recommendations on deformable image registration uncertainties for radiotherapy applications

Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.

Case Report: Cumulative proton dose reconstruction using CBCT-based synthetic CT for interfraction metallic port variability in breast tissue expanders

Introduction

Dose perturbation of spot-scanning proton beams passing through a dislocated metallic port (MP) of a breast tissue expander may degrade target dose coverage or deliver excess dose to the ipsilateral lung and heart. The feasibility of utilizing daily cone-beam computed tomography (CBCT)-based synthetic CTs (synCTs) for dose reconstruction was evaluated, and the fractional and cumulative dosimetric impact due to daily MP dislocation is reported.

Methods

The synCT was generated by deforming the simulation CT to daily CBCT. The MP structure template was mapped onto all CTs on the basis of daily MP position. Proton treatment plans were generated with two and three fields on the planned CT (pCT, Plan A) and the first verification CT (vCT, Plan B), respectively, for a fractional dose of 1.8 Gy(RBE). Plan A and Plan B were used alternatively, as determined by the daily MP position. The reconstructed fractional doses were calculated with corresponding plans and synCTs, and the cumulative doses were summed with the rigid or deformed fractional doses on pCT and vCT.

Results

The planned and reconstructed fractional dose demonstrated a low-dose socket around the planned MP position due to the use of field-specific targets (FSTs). Dose hot spots with >120% of the prescription due to MP dislocation were found behind the planned MP position on most reconstructed fractional doses. The reconstructed cumulative dose shows two low-dose sockets around the two planned MP positions reflecting the two plans used. The doses at the hot spots behind the planned MPs averaged out to 114% of the prescription. The cumulative D95% of the CTV_Chest Wall decreased by up to 2.4% and 4.0%, and the cumulative V20Gy(RBE) of the left lung decreased to 16.1% and 16.8% on pCT and vCT, respectively. The cumulative Dmean of the heart decreased to as low as 0.7 Gy(RBE) on pCT but increased to as high as 1.6 Gy(RBE) on vCT.

Conclusion

The robustness of proton plans using FSTs around the magnet in the MP of the tissue expander can be improved by applying multiple fields and plans, which provides forgiveness of dose heterogeneity incurred from dislocation of high-Z materials in this single case.

Evaluating synthetic computed tomography images for adaptive radiotherapy decision making in head and neck cancer

Background and purpose

Adaptive radiotherapy (ART) decision-making benefits from dosimetric information to supplement image inspection when assessing the significance of anatomical changes. This study evaluated a dosimetry-based clinical decision workflow for ART utilizing deformable registration of the original planning computed tomography (CT) image to the daily Cone Beam CT (CBCT) to replace the need for a replan CT for dose estimation.

Materials and methods

We used 12 retrospective Head & Neck patient cases having a ground truth - a replan CT (rCT) in response to anatomical changes apparent in the daily CBCT - to evaluate the accuracy of dosimetric assessment conducted on synthetic CTs (sCT) generated by deforming the original planning CT Hounsfield Units to the daily CBCT anatomy.The original plan was applied to the sCT and dosimetric accuracy of the sCT was assessed by analyzing plan objectives for targets and organs-at-risk compared to calculations on the ground-truth rCT. Three commercial DIR algorithms were compared.

Results

For the best-performing algorithms, the majority of dose metrics calculated on the sCTs differed by less than 4 Gy (5.7% of 70 Gy prescription dose). An uncertainty of ±2.5 Gy (3.6% of 70 Gy prescription) is recommended as a conservative tolerance when evaluating dose metrics on sCTs for head and neck.

Conclusions

Synthetic CTs present a valuable addition to the adaptive radiotherapy workflow, and synthetic CT dose estimates can be effectively used in addition to the current practice of visually inspecting the overlay of the planning CT and CBCT to assess the significance of anatomical change.

Fan beam CT-guided online adaptive external radiotherapy of uterine cervical cancer: a dosimetric evaluation

PURPOSE

To discuss the dosimetric advantages and reliability of the accurate delivery of online adaptive radiotherapy(online ART) for uterine cervical cancer(UCC).

METHODS AND MATERIALS

Six UCC patients were enrolled in this study. 95% of the planning target volume (PTV) reached 100% of the prescription dose (50.4 Gy/28fractions/6weeks) was required. The patients were scanned with uRT-Linac 506c KV-FBCT then the target volume (TV) and organs at risk (OARs) were delineated by doctors. The dosimeters designed and obtained a routine plan (Plan0). KV-FBCT was used for image guidance before subsequent fractional treatment. The online ART was processed after registration, which acquired a virtual nonadaptive radiotherapy plan (VPlan) and an adaptive plan (APlan). VPlan was the direct calculation of Plan0 on the fractional image, while APlan required adaptive optimization and calculation. In vivo dose monitoring and three-dimensional dose reconstruction were required during the implementation of APlan.

RESULTS

The inter-fractional volumes of the bladder and rectum changed greatly among the treatments. These changes influenced the primary gross tumor volume (GTVp) and the position deviation of GTVp and PTV and positively affected the prescription dose coverage of TV. GTVp decreased gradually along with dose accumulation. The Dmax, D98, D95, D50, and D2 of APlan were superior to those of VPlan in target dose distribution. APlan had good conformal index, homogeneity index and target coverage. The rectum V40 and Dmax, bladder V40, the small bowel V40 and Dmax of APlan were better than that of VPlan. The APlan's fractional mean γ passing rate was significantly higher than the international standard and the mean γ passing rate of all cases after the three-dimensional reconstruction was higher than 97.0%.

CONCLUSION

Online ART in external radiotherapy of UCC significantly improved the dose distribution and can become an ideal technology to achieve individualized precise radiotherapy.

The geometric and dosimetric accuracy of kilovoltage cone beam computed tomography images for adaptive treatment: a systematic review

Objectives

To provide an overview and meta-analysis of different techniques adopted to accomplish kVCBCT for dose calculation and automated segmentation.

Methods

A systematic review and meta-analysis were performed on eligible studies demonstrating kVCBCT-based dose calculation and automated contouring of different tumor features. Meta-analysis of the performance was accomplished on the reported γ analysis and dice similarity coefficient (DSC) score of both collected results as three subgroups (head and neck, chest, and abdomen).

Results

After the literature scrutinization (n = 1008), 52 papers were recognized for the systematic review. Nine studies of dosimtric studies and eleven studies of geometric analysis were suitable for inclusion in meta-analysis. Using kVCBCT for treatment replanning depends on a method used. Deformable Image Registration (DIR) methods yielded small dosimetric error (≤2%), γ pass rate (≥90%) and DSC (≥0.8). Hounsfield Unit (HU) override and calibration curve-based methods also achieved satisfactory yielded small dosimetric error (≤2%) and γ pass rate ((≥90%), but they are prone to error due to their sensitivity to a vendor-specific variation in kVCBCT image quality.

Conclusions

Large cohorts of patients ought to be undertaken to validate methods achieving low levels of dosimetric and geometric errors. Quality guidelines should be established when reporting on kVCBCT, which include agreed metrics for reporting on the quality of corrected kVCBCT and defines protocols of new site-specific standardized imaging used when obtaining kVCBCT images for adaptive radiotherapy.

Advances in knowledge

This review gives useful knowledge about methods making kVCBCT feasible for kVCBCT-based adaptive radiotherapy, simplifying patient pathway and reducing concomitant imaging dose to the patient.

Deep learning based synthetic CT from cone beam CT generation for abdominal paediatric radiotherapy

Objective. Adaptive radiotherapy workflows require images with the quality of computed tomography (CT) for re-calculation and re-optimisation of radiation doses. In this work we aim to improve the quality of on-board cone beam CT (CBCT) images for dose calculation using deep learning.Approach. We propose a novel framework for CBCT-to-CT synthesis using cycle-consistent Generative Adversarial Networks (cycleGANs). The framework was tailored for paediatric abdominal patients, a challenging application due to the inter-fractional variability in bowel filling and small patient numbers. We introduced to the networks the concept of global residuals only learning and modified the cycleGAN loss function to explicitly promote structural consistency between source and synthetic images. Finally, to compensate for the anatomical variability and address the difficulties in collecting large datasets in the paediatric population, we applied a smart 2D slice selection based on the common field-of-view (abdomen) to our imaging dataset. This acted as a weakly paired data approach that allowed us to take advantage of scans from patients treated for a variety of malignancies (thoracic-abdominal-pelvic) for training purposes. We first optimised the proposed framework and benchmarked its performance on a development dataset. Later, a comprehensive quantitative evaluation was performed on an unseen dataset, which included calculating global image similarity metrics, segmentation-based measures and proton therapy-specific metrics.Main results. We found improved performance for our proposed method, compared to a baseline cycleGAN implementation, on image-similarity metrics such as Mean Absolute Error calculated for a matched virtual CT (55.0 ± 16.6 HU proposed versus 58.9 ± 16.8 HU baseline). There was also a higher level of structural agreement for gastrointestinal gas between source and synthetic images measured using the dice similarity coefficient (0.872 ± 0.053 proposed versus 0.846 ± 0.052 baseline). Differences found in water-equivalent thickness metrics were also smaller for our method (3.3 ± 2.4% proposed versus 3.7 ± 2.8% baseline).Significance. Our findings indicate that our innovations to the cycleGAN framework improved the quality and structure consistency of the synthetic CTs generated.

Practical and technical key challenges in head and neck adaptive radiotherapy: The GORTEC point of view

Anatomical variations occur during head and neck (H&N) radiotherapy (RT) treatment. These variations may result in underdosage to the target volume or overdosage to the organ at risk. Replanning during the treatment course can be triggered to overcome this issue. Due to technological, methodological and clinical evolutions, tools for adaptive RT (ART) are becoming increasingly sophisticated. The aim of this paper is to give an overview of the key steps of an H&N ART workflow and tools from the point of view of a group of French-speaking medical physicists and physicians (from GORTEC). Focuses are made on image registration, segmentation, estimation of the delivered dose of the day, workflow and quality assurance for an implementation of H&N offline and online ART. Practical recommendations are given to assist physicians and medical physicists in a clinical workflow.

CBCT-guided adaptive radiotherapy using self-supervised sequential domain adaptation with uncertainty estimation

Adaptive radiotherapy (ART) is an advanced technology in modern cancer treatment that incorporates progressive changes in patient anatomy into active plan/dose adaption during the fractionated treatment. However, the clinical application relies on the accurate segmentation of cancer tumors on low-quality on-board images, which has posed challenges for both manual delineation and deep learning-based models. In this paper, we propose a novel sequence transduction deep neural network with an attention mechanism to learn the shrinkage of the cancer tumor based on patients' weekly cone-beam computed tomography (CBCT). We design a self-supervised domain adaption (SDA) method to learn and adapt the rich textural and spatial features from pre-treatment high-quality computed tomography (CT) to CBCT modality in order to address the poor image quality and lack of labels. We also provide uncertainty estimation for sequential segmentation, which aids not only in the risk management of treatment planning but also in the calibration and reliability of the model. Our experimental results based on a clinical non-small cell lung cancer (NSCLC) dataset with sixteen patients and ninety-six longitudinal CBCTs show that our model correctly learns weekly deformation of the tumor over time with an average dice score of 0.92 on the immediate next step, and is able to predict multiple steps (up to 5 weeks) for future patient treatments with an average dice score reduction of 0.05. By incorporating the tumor shrinkage predictions into a weekly re-planning strategy, our proposed method demonstrates a significant decrease in the risk of radiation-induced pneumonitis up to 35% while maintaining the high tumor control probability.

CBCT-based synthetic CT generated using CycleGAN with HU correction for adaptive radiotherapy of nasopharyngeal carcinoma

This study aims to utilize a hybrid approach of phantom correction and deep learning for synthesized CT (sCT) images generation based on cone-beam CT (CBCT) images for nasopharyngeal carcinoma (NPC). 52 CBCT/CT paired images of NPC patients were used for model training (41), validation (11). Hounsfield Units (HU) of the CBCT images was calibrated by a commercially available CIRS phantom. Then the original CBCT and the corrected CBCT (CBCT_cor) were trained separately with the same cycle generative adversarial network (CycleGAN) to generate SCT1 and SCT2. The mean error and mean absolute error (MAE) were used to quantify the image quality. For validations, the contours and treatment plans in CT images were transferred to original CBCT, CBCT_cor, SCT1 and SCT2 for dosimetric comparison. Dose distribution, dosimetric parameters and 3D gamma passing rate were analyzed. Compared with rigidly registered CT (RCT), the MAE of CBCT, CBCT_cor, SCT1 and SCT2 were 346.11 ± 13.58 HU, 145.95 ± 17.64 HU, 105.62 ± 16.08 HU and 83.51 ± 7.71 HU, respectively. Moreover, the average dosimetric parameter differences for the CBCT_cor, SCT1 and SCT2 were 2.7% ± 1.4%, 1.2% ± 1.0% and 0.6% ± 0.6%, respectively. Using the dose distribution of RCT images as reference, the 3D gamma passing rate of the hybrid method was significantly better than the other methods. The effectiveness of CBCT-based sCT generated using CycleGAN with HU correction for adaptive radiotherapy of nasopharyngeal carcinoma was confirmed. The image quality and dose accuracy of SCT2 were outperform the simple CycleGAN method. This finding has great significance for the clinical application of adaptive radiotherapy for NPC.

Seq2Morph: A deep learning deformable image registration algorithm for longitudinal imaging studies and adaptive radiotherapy

PURPOSE

To simultaneously register all the longitudinal images acquired in a radiotherapy course for analyzing patients' anatomy changes for adaptive radiotherapy (ART).

METHODS

To address the unique needs of ART, we designed Seq2Morph, a novel deep learning-based deformable image registration (DIR) network. Seq2Morph was built upon VoxelMorph which is a general-purpose framework for learning-based image registration. The major upgrades are (1) expansion of inputs to all weekly cone-beam computed tomography (CBCTs) acquired for monitoring treatment responses throughout a radiotherapy course, for registration to their planning CT; (2) incorporation of 3D convolutional long short-term memory between the encoder and decoder of VoxelMorph, to parse the temporal patterns of anatomical changes; and (3) addition of bidirectional pathways to calculate and minimize inverse consistency errors (ICEs). Longitudinal image sets from 50 patients, including a planning CT and 6 weekly CBCTs per patient, were utilized for network training and cross-validation. The outputs were deformation vector fields for all the registration pairs. The loss function was composed of a normalized cross-correlation for image intensity similarity, a DICE for contour similarity, an ICE, and a deformation regularization term. For performance evaluation, DICE and Hausdorff distance (HD) for the manual versus predicted contours of tumor and esophagus on weekly basis were quantified and further compared with other state-of-the-art algorithms, including conventional VoxelMorph and large deformation diffeomorphic metric mapping (LDDMM).

RESULTS

Visualization of the hidden states of Seq2Morph revealed distinct spatiotemporal anatomy change patterns. Quantitatively, Seq2Morph performed similarly to LDDMM, but significantly outperformed VoxelMorph as measured by GTV DICE: (0.799±0.078, 0.798±0.081, and 0.773±0.078), and 50% HD (mm): (0.80±0.57, 0.88±0.66, and 0.95±0.60). The per-patient inference of Seq2Morph took 22 s, much less than LDDMM (∼30 min).

CONCLUSIONS

Seq2Morph can provide accurate and fast DIR for longitudinal image studies by exploiting spatial-temporal patterns. It closely matches the clinical workflow and has the potential to serve both online and offline ART.

Evaluation of Dose Calculation Based on Cone-Beam CT Using Different Measuring Correction Methods for Head and Neck Cancer Patients

Purpose: To investigate and compare 2 cone-beam computed tomography (CBCT) correction methods for CBCT-based dose calculation. Materials and Methods: Routine CBCT image sets of 12 head and neck cancer patients who received volumetric modulated arc therapy (VMAT) treatment were retrospectively analyzed. The CBCT images obtained using an on-board imager (OBI) at the first treatment fraction were firstly deformable registered and padded with the kVCT images to provide enough anatomical information about the tissues for dose calculation. Then, 2 CBCT correction methods were developed and applied to correct CBCT Hounsfield unit (HU) values. One method (HD method) is based on protocol-specific CBCT HU to physical density (HD) curve, and the other method (HM method) is based on histogram matching (HM) of HU value. The corrected CBCT images (CBCT_HD and CBCT_HM for HD and HM methods) were imported into the original planning system for dose calculation based on the HD curve of kVCT (the planning CT). The dose computation result was analyzed and discussed to compare these 2 CBCT-correction methods. Results: Dosimetric parameters, such as the D_mean, D_max and D_5% of the target volume in CBCT plan doses, were higher than those in the kVCT plan doses; however, the deviations were less than 2%. The D_2%, in parallel organs such as the parotid glands, the deviations from the CBCT_HM plan dose were less than those of the CBCT_HD plan dose. The differences were statistically significant (P < .05). Meanwhile, the V₃₀ value based on the HM method was better than that based on the HD method in the oral cavity region (P = .016). In addition, we also compared the γ passing rates of kVCT plan doses with the 2 CBCT plan doses, and negligible differences were found. Conclusion: The HM method was more suitable for head and neck cancer patients than the HD one. Furthermore, with the CBCT_HM-based method, the dose calculation result better matches the kVCT-based dose calculation.

Dosimetric comparison of deformable image registration and synthetic CT generation based on CBCT images for organs at risk in cervical cancer radiotherapy

OBJECTIVE

Anatomical variations existing in cervical cancer radiotherapy treatment can be monitored by cone-beam computed tomography (CBCT) images. Deformable image registration (DIR) from planning CT (pCT) to CBCT images and synthetic CT (sCT) image generation based on CBCT are two methods for improving the quality of CBCT images. This study aims to compare the accuracy of these two approaches geometrically and dosimetrically in cervical cancer radiotherapy.

METHODS

In this study, 40 paired pCT-CBCT images were collected to evaluate the accuracy of DIR and sCT generation. The DIR method was based on a 3D multistage registration network that was trained with 150 paired pCT-CBCT images, and the sCT generation method was performed based on a 2D cycle-consistent adversarial network (CycleGAN) with 6000 paired pCT-CBCT slices for training. Then, the doses were recalculated with the CBCT, pCT, deformed pCT (dpCT) and sCT images by a GPU-based Monte Carlo dose code, ArcherQA, to obtain Dose_CBCT, Dose_pCT, Dose_dpCT and Dose_sCT. Organs at risk (OARs) included small intestine, rectum, bladder, spinal cord, femoral heads and bone marrow, CBCT and pCT contours were delineated manually, dpCT contours were propagated through deformation vector fields, sCT contours were auto-segmented and corrected manually.

RESULTS

The global gamma pass rate of Dose_sCT and Dose_dpCT was 99.66% ± 0.34%, while that of Dose_CBCT and Dose_dpCT was 85.92% ± 7.56% at the 1%/1 mm criterion and a low-dose threshold of 10%. Based on Dose_dpCT as uniform dose distribution, there were comparable errors in femoral heads and bone marrow for the dpCT and sCT contours compared with CBCT contours, while sCT contours had lower errors in small intestine, rectum, bladder and spinal cord, especially for those with large volume difference of pCT and CBCT.

CONCLUSIONS

For cervical cancer radiotherapy, the DIR method and sCT generation could produce similar precise dose distributions, but sCT contours had higher accuracy when the difference in planning CT and CBCT was large.

Enhancement of cone beam CT image registration by super-resolution pre-processing algorithm

In order to enhance cone-beam computed tomography (CBCT) image information and improve the registration accuracy for image-guided radiation therapy, we propose a super-resolution (SR) image enhancement method. This method uses super-resolution techniques to pre-process the CBCT prior to registration. Three rigid registration methods (rigid transformation, affine transformation, and similarity transformation) and a deep learning deformed registration (DLDR) method with and without SR were compared. The five evaluation indices, the mean squared error (MSE), mutual information, Pearson correlation coefficient (PCC), structural similarity index (SSIM), and PCC + SSIM, were used to validate the results of registration with SR. Moreover, the proposed method SR-DLDR was also compared with the VoxelMorph (VM) method. In rigid registration with SR, the registration accuracy improved by up to 6% in the PCC metric. In DLDR with SR, the registration accuracy was improved by up to 5% in PCC + SSIM. When taking the MSE as the loss function, the accuracy of SR-DLDR is equivalent to that of the VM method. In addition, when taking the SSIM as the loss function, the registration accuracy of SR-DLDR is 6% higher than that of VM. SR is a feasible method to be used in medical image registration for planning CT (pCT) and CBCT. The experimental results show that the SR algorithm can improve the accuracy and efficiency of CBCT image alignment regardless of which alignment algorithm is used.

Daily dose evaluation based on corrected CBCTs for breast cancer patients: accuracy of dose and complication risk assessment

OBJECTIVES

The goal of this study is to validate different CBCT correction methods to select the superior method that can be used for dose evaluation in breast cancer patients with large anatomical changes treated with photon irradiation.

MATERIALS AND METHOD

Seventy-six breast cancer patients treated with a partial VMAT photon technique (70% conformal, 30% VMAT) were included in this study. All patients showed at least a 5 mm variation (swelling or shrinkage) of the breast on the CBCT compared to the planning-CT (pCT) and had a repeat-CT (rCT) for dose evaluation acquired within 3 days of this CBCT. The original CBCT was corrected using four methods: (1) HU-override correction (CBCT_HU), (2) analytical correction and conversion (CBCT_CC), (3) deep learning (DL) correction (CT_DL) and (4) virtual correction (CT_V). Image quality evaluation consisted of calculating the mean absolute error (MAE) and mean error (ME) within the whole breast clinical target volume (CTV) and the field of view of the CBCT minus 2 cm (CBCT-ROI) with respect to the rCT. The dose was calculated on all image sets using the clinical treatment plan for dose and gamma passing rate analysis.

RESULTS

The MAE of the CBCT-ROI was below 66 HU for all corrected CBCTs, except for the CBCT_HU with a MAE of 142 HU. No significant dose differences were observed in the CTV regions in the CBCT_CC, CT_DL and CT_v. Only the CBCT_HU deviated significantly (p < 0.01) resulting in 1.7% (± 1.1%) average dose deviation. Gamma passing rates were > 95% for 2%/2 mm for all corrected CBCTs.

CONCLUSION

The analytical correction and conversion, deep learning correction and virtual correction methods can be applied for an accurate CBCT correction that can be used for dose evaluation during the course of photon radiotherapy of breast cancer patients.

Technical note: Characterization of novel iterative reconstructed cone beam CT images for dose tracking and adaptive radiotherapy on L-shape linacs

BACKGROUND

Cone-beam computed tomography (CBCT) allows for patient setup and positioning, and potentially dose verification or adaptive replanning prior to each treatment delivery. Poor CBCT image quality due to scatter artifacts and patient motion has been a major limiting factor. A new image reconstruction algorithm was recently clinically implemented for improving image quality through iterative reconstruction (iCBCT).

PURPOSE

This study aims to characterize iCBCT image quality, establish image value (HU)-to-relative electron density (RED) calibration curves for dose calculation, and assess the dosimetric accuracy for different anatomical sites.

MATERIAL AND METHODS

Both conventional CBCT and iCBCT scans were acquired from a Varian TrueBeam On-Board Imager system. A Catphan 604 phantom was scanned to compare image quality between the traditional Feldkamp-Davis-Kress (FDK) and novel iterative reconstruction techniques. Computerized Imaging Reference Systems (CIRS) electron density phantom was used to construct site-specific HU-RED curves corresponding to various scan settings. The CIRS Dynamic Thorax phantom, Rando pelvis phantom, and BrainLab head phantom were used for assessing dosimetric accuracy calculated on iCBCT images, compared to that on traditional FDK-based CBCT images. All phantoms were scanned on a computed tomography (CT) to obtain baseline HU values for comparison.

RESULTS

Test results obtained from Catphan showed statistically significant improvement with iCBCT, compared to FDK CBCT. Average HU differences from the baseline CT values were improved to within ±30 HU for iCBCT, compared to FDK CBCT for phantom studies. Dose calculated on iCBCT for both phantoms and patient cases directly using baseline HU-RED calibration from CT showed 0.5%-2.0% accuracy from the baseline dose calculated on CT, which is comparable to doses calculated using site-specific HU-RED calibration curves.

CONCLUSION

iCBCT provides improved image quality, improved HU accuracy compared to CT baseline, and has potential to provide online dose verification as part of the adaptive radiotherapy workflow directly using the baseline HU-RED calibration curve from CT.

A clinically relevant online patient QA solution with daily CT scans and EPID-based in vivo dosimetry: a feasibility study on rectal cancer

Objective. Adaptive radiation therapy (ART) could protect organs at risk (OARs) while maintain high dose coverage to targets. However, there is still a lack of efficient online patient quality assurance (QA) methods, which is an obstacle to large-scale adoption of ART. We aim to develop a clinically relevant online patient QA solution for ART using daily CT scans and EPID-based in vivo dosimetry. Approach. Ten patients with rectal cancer at our center were included. Patients’ daily CT scans and portal images were collected to generate reconstructed 3D dose distributions. Contours of targets and OARs were recontoured on these daily CT scans by a clinician or an auto-segmentation algorithm, then dose-volume indices were calculated, and the percent deviation of these indices to their original plans were determined. This deviation was regarded as the metric for clinically relevant patient QA. The tolerance level was obtained using a 95% confidence interval of the QA metric distribution. These deviations could be further divided into anatomically relevant or delivery relevant indicators for error source analysis. Finally, our QA solution was validated on an additional six clinical patients. Main results. In rectal cancer, the 95% confidence intervals of the QA metric for PTV ΔD 95 (%) were [−3.11%, 2.35%], and for PTV ΔD 2 (%) were [−0.78%, 3.23%]. In validation, 68% for PTV ΔD 95 (%), and 79% for PTV ΔD 2 (%) of the 28 fractions are within tolerances of the QA metrics. one patient’s dosimetric impact of anatomical variations during treatment were observed through the source of error analysis. Significance. The online patient QA solution using daily CT scans and EPID-based in vivo dosimetry is clinically feasible. Source of error analysis has the potential for distinguishing sources of error and guiding ART for future treatments.

Use of CBCT plus plan robustness for reducing QACT frequency in intensity-modulated proton therapy: Head-and-neck cases

PURPOSE

Anatomic variation has a significant dosimetric impact in intensity-modulated proton therapy. Weekly or biweekly computed tomography (CT) scans, called quality assurance CTs (QACTs), are used to monitor anatomic and resultant dose changes to determine whether adaptive plans are needed. Frequent CT scans result in unwanted QACT dose and increased clinical workloads. This study proposed utilizing patient setup cone-beam CTs (CBCTs) and treatment plan robustness to reduce the frequency of QACTs.

METHODS

We retrospectively analyzed data from 27 patients with head-and-neck cancer, including 594 CBCTs, 136 QACTs, and 19 adaptive plans. For each CBCT, water-equivalent thickness (WET) along the pencil-beam path was calculated. For each treatment plan, the WET of the first-day CBCT was used as the reference, and the mean WET changes (ΔWET) in each following CBCT was used as the surrogate of proton range change. Using CBCTs acquired prior to a QACT, we predicted the ΔWET on the QACT day by a linear regression model. The impact of range change on target dose was calculated as the predicted ΔWET weighted by the monitor units of each field. In addition, plan robustness was estimated from the robust dose-volume histograms (DVHs) and combined with ΔWET to reduce QACT frequency. Robustness was estimated from the distance between the DVH curves of the nominal and worst scenarios.

RESULTS

When the estimated mean ΔWET was <6.5 mm (or <7.5 mm if the robustness was >95%), the QACT could be skipped without missing any adaptive planning; otherwise a QACT was required. Overall, 41% of QACTs could be eliminated when ΔWET was <6.5 mm and 56% when ΔWET was <7.5 mm, and robustness was >95%. At least one QACT could have been omitted in 25 of the 27 cases under skipping thresholds at ΔWETs <7.5 mm and R > 95%.

CONCLUSION

This study suggests that the number of QACTs can be greatly reduced by calculating range change in patient setup CBCTs and can be further reduced by combining this information with analyses of plan robustness.

Effect of subject motion and gantry rotation speed on image quality and dose delivery in CT-guided radiotherapy

PURPOSE

To investigate the effects of subject motion and gantry rotation speed on computed tomography (CT) image quality over a range of image acquisition speeds for fan-beam (FB) and cone-beam (CB) CT scanners, and quantify the geometric and dosimetric errors introduced by FB and CB sampling in the context of adaptive radiotherapy.

METHODS

Images of motion phantoms were acquired using four CT scanners with gantry rotation speeds of 0.5 s/rotation (denoted FB-0.5), 1.9 s/rotation (FB-1.9), 16.6 s/rotation (CB-16.6), and 60.0 s/rotation (CB-60.0). A phantom presenting various tissue densities undergoing motion with 4-s period and ranging in amplitude from ±0.5 to ±10.0 mm was used to characterize motion artifacts (streaks), motion blur (edge-spread function, ESF), and geometric inaccuracy (excursion of insert centroids and distortion of known shape). An anthropomorphic abdomen phantom undergoing ±2.5-mm motion with 4-s period was used to simulate an adaptive radiotherapy workflow, and relative geometric and dosimetric errors were compared between scanners.

RESULTS

At ±2.5-mm motion, phantom measurements demonstrated mean ± SD ESF widths of 0.6 ± 0.0, 1.3 ± 0.4, 2.0 ± 1.1, and 2.9 ± 2.0 mm and geometric inaccuracy (excursion) of 2.7 ± 0.4, 4.1 ± 1.2, 2.6 ± 0.7, and 2.0 ± 0.5 mm for the FB-0.5, FB-1.9, CB-16.6, and CB-60.0 scanners, respectively. The results demonstrated nonmonotonic trends with scanner speed for FB and CB geometries. Geometric and dosimetric errors in adaptive radiotherapy plans were largest for the slowest (CB-60.0) scanner and similar for the three faster systems (CB-16.6, FB-1.9, and FB-0.5).

CONCLUSIONS

Clinically standard CB-60.0 demonstrates strong image quality degradation in the presence of subject motion, which is mitigated through faster CBCT or FBCT. Although motion blur is minimized for FB-0.5 and FB-1.9, such systems suffer from increased geometric distortion compared to CB-16.6. Each system reflects tradeoffs in image artifacts and geometric inaccuracies that affect treatment delivery/dosimetric error and should be considered in the design of next-generation CT-guided radiotherapy systems.

Comprehensive Analysis of Set-Up Gain of 6-Dimensional Cone-Beam CT Correction Method in Radiotherapy for Head and Neck and Brain Tumors

This study quantitatively analyzed the gain of the six-dimensional (6D) cone-beam CT (CBCT) correction method compared with the conventional set-up method in 60 patients who underwent radiation treatment of head and neck and brain tumors. The correction gain of CBCT was calculated for the translational and rotational motion components separately and in combination to evaluate the individual and overall effects of these motion components. Using a statistical simulation mimicking the actual set-up correction process, the effective gain of periodic CBCT correction during the entire treatment fraction was analyzed by target size and CBCT correction period under two different correction scenarios: translation alone and full 6D corrections. From the analyses performed in this study, the gain of CBCT correction was quantitatively determined for each situation, and the appropriate CBCT correction strategy was suggested based on treatment purpose and target size.

Comparison of Three Commercial Methods of Cone-Beam Computed Tomography-Based Dosimetric Analysis of Head-and-Neck Patients with Weight Loss

Purpose

This investigation compares three commercial methods of cone-beam computed tomography (CBCT)-based dosimetric analysis to a method based on repeat computed tomography (CT).

Materials and Methods

Seventeen head-and-neck patients treated in 2020, and with a repeat CT, were included in the analyses. The planning CT was deformed to anatomy in repeat CT to generate a reference plan. Two of the CBCT-based methods generated test plans by deforming the planning CT to CBCT of fraction N using VelocityAI™ and SmartAdapt®. The third method compared directly calculated doses on the CBCT for fraction 1 and fraction N, using PerFraction™. Maximum dose to spinal cord (Cord_dmax) and dose to 95% volume (D95) of planning target volumes (PTVs) were used to assess "need to replan" criteria.

Results

The VelocityAI™ method provided results that most accurately matched the reference plan in "need to replan" criteria using either Cord_dmax or PTV D95. SmartAdapt® method overestimated the change in Cord_dmax (6.77% vs. 3.85%, P < 0.01) and change in cord volume (9.56% vs. 0.67%, P < 0.01) resulting in increased false positives in "need to replan" criteria, and performed similarly to VelocityAI™ for D95, but yielded more false negatives. PerFraction™ method underestimated Cord_dmax, did not perform any volume deformation, and missed all "need to replan" cases based on cord dose. It also yielded high false negatives using the D95 PTV criteria.

Conclusions

The VelocityAI™-based method using fraction N CBCT is most similar to the reference plan using repeat CT; the other two methods had significant differences.

Evaluation of the clinical impact of the differences between planned and delivered dose in prostate cancer radiotherapy based on CT-on-rails IGRT and patient-reported outcome scores

PURPOSE

To estimate the clinical impact of differences between delivered and planned dose using dose metrics and normal tissue complication probability (NTCP) modeling.

METHODS

Forty-six consecutive patients with prostate adenocarcinoma between 2010 and 2015 treated with intensity-modulated radiation therapy (IMRT) and who had undergone computed tomography on rails imaging were included. Delivered doses to bladder and rectum were estimated using a contour-based deformable image registration method. The bladder and rectum NTCP were calculated using dose-response parameters applied to planned and delivered dose distributions. Seven urinary and gastrointestinal symptoms were prospectively collected using the validated prostate cancer symptom indices patient reported outcome (PRO) at pre-treatment, weekly treatment, and post-treatment follow-up visits. Correlations between planned and delivered doses against PRO were evaluated in this study.

RESULTS

Planned mean doses to bladder and rectum were 44.9 ± 13.6 Gy and 42.8 ± 7.3 Gy, while delivered doses were 46.1 ± 13.4 Gy and 41.3 ± 8.7 Gy, respectively. D_10cc for rectum was 64.1 ± 7.6 Gy for planned and 60.1 ± 9.3 Gy for delivered doses. NTCP values of treatment plan were 22.3% ± 8.4% and 12.6% ± 5.9%, while those for delivered doses were 23.2% ± 8.4% and 9.9% ± 8.3% for bladder and rectum, respectively. Seven of 25 patients with follow-up data showed urinary complications (28%) and three had rectal complications (12%). Correlations of NTCP values of planned and delivered doses with PRO follow-up data were random for bladder and moderate for rectum (0.68 and 0.67, respectively).

CONCLUSION

Sensitivity of bladder to clinical variations of dose accumulation indicates that an automated solution based on a DIR that considers inter-fractional organ deformation could recommend intervention. This is intended to achieve additional rectum sparing in cases that indicate higher than expected dose accumulation early during patient treatment in order to prevent acute severity of bowel symptoms.

Impact of intrafraction changes in delivered dose of the day for prostate cancer patients treated with stereotactic body radiotherapy via MR-Linac

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Beam on MR acquisition on the MR-Linac can be used to compute DDOTD.

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Intrafraction motion via volumetric variability of OARs can impact dosimetry.

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Computation of the DDOTD may help inform prospective fractions for SBRT prostate.

Purpose

The purpose of this study is to evaluate the impact of intrafraction pelvic motion by comparing the adapted plan dose (APD) and the computed delivered dose of the day (DDOTD) for patients with prostate cancer (PCa) treated with SBRT on the MR-Linac.

Methods

Twenty patients with PCa treated with MR-guided adaptive SBRT were included. A 9-field IMRT distribution was adapted based on the anatomy of the day to deliver a total prescription dose of 3000 cGy in 5 fractions to the prostate plus a 5 mm isotropic margin. Prostate, bladder, and rectum were re-contoured on the MR-image acquired during treatment delivery (MR_BO). DDOTD was computed by propagating the dose from the daily adapted plan generated during treatment onto the MR_BO.

Results

Target coverage was met for all fractions, however, computed DDOTD was significantly less than the APD (p < 0.05). During an average treatment of 53 min, mean bladder volume increased by 116%, which led to a significant decrease in the DDOTD bladder D40% (p < 0.001). However, DDOTD to bladder 5 cc was significantly higher (p < 0.001) than APD. Rectum intrafraction changes were observed based on a volume change of −20% to 83% and presence of significant dose changes from APD to DDOTD for rectum D20% (p < 0.05) and D1cc (p < 0.0001).

Conclusions

Intrafraction motion observed during prostate SBRT treatment on the MR-Linac have dosimetric impacts on both the target and organs at risk. Post-treatment computation using DDOTD may inform adaptation beyond anatomic changes in subsequent treatment fractions to best capitalize on MR-Linac technology and widen the therapeutic index of SBRT for PCa.

As Easy as 1, 2, 3? How to Determine CBCT Frequency in Adjuvant Breast Radiotherapy

The current study aims to assess the suitability of setup errors during the first three treatment fractions to determine cone-beam computed tomography (CBCT) frequency in adjuvant breast radiotherapy. For this, 45 breast cancer patients receiving non-hypofractionated radiotherapy after lumpectomy, including a simultaneous integrated boost (SIB) to the tumor bed and daily CBCT imaging, were retrospectively selected. In a first step, mean and maximum setup errors on treatment days 1–3 were correlated with the mean setup errors during subsequent treatment days. In a second step, dose distribution was estimated using a dose accumulation workflow based on deformable image registration, and setup errors on treatment days 1–3 were correlated with dose deviations in the clinical target volumes (CTV) and organs at risk (OAR). No significant correlation was found between mean and maximum setup errors on treatment days 1–3 and mean setup errors during subsequent treatment days. In addition, mean and maximum setup errors on treatment days 1–3 correlated poorly with dose coverage of the CTVs and dose to the OARs. Thus, CBCT frequency in adjuvant breast radiotherapy should not be determined solely based on the magnitude of setup errors during the first three treatment fractions.

Adaptive replanning using cone beam CT for deformation of original CT simulation

BACKGROUND

During a course of radiation therapy, anatomical changes such as a decrease in tumour size or weight loss can trigger the need for repeating a computed tomography (CT) simulation scan in order to generate a new treatment plan. This adaptive approach requires a separate appointment for an additional CT scan which generates additional burden, cost, and radiation exposure for patients.

CASE PRESENTATION

Here, we present a case of a head and neck cancer patient who required palliative radiation for a large neck mass. During treatment, he had a remarkable response which required a replan due to rapid tumour downsizing. In this case, we used a novel technique to avoid repeating the planning CT simulation by using a mid-treatment high-quality cone beam CT (CBCT) to deform the secondary image (plan CT) of the original planning CT and generate a new adapted treatment plan.

CONCLUSION

This is the first report to our knowledge using a Halcyon CBCT to deform the original planning CT in order to generate a new radiation treatment plan, and this novel technique represents a new potential method of adaptive replanning for select patients.

A prediction model for dosimetric-based lung adaptive radiotherapy

PURPOSE

Anatomical changes occurred during the treatment course of radiation therapy for lung cancer patients may introduce clinically unacceptable dosimetric deviations from the planned dose. Adaptive radiotherapy (ART) can compensate these dosimetric deviations in subsequent treatments via plan adaption. Determining whether and when to trigger plan adaption during the treatment course is essential to the effectiveness and efficiency of ART. In this study, we aimed to develop a prediction model as an auxiliary decision-making tool for lung ART to identify the patients with intrathoracic anatomical changes that would potentially benefit from the plan adaptions during the treatment course.

METHODS

Seventy-one pairs of weekly cone-beam computer tomography (CBCT) and planning CT (pCT) from 17 advanced non-small cell lung cancer patients were enrolled in this study. To assess the dosimetric impacts brought by anatomical changes observed on each CBCT, dose distribution of the original treatment plan on the CBCT anatomy was calculated on a virtual CT generated by deforming the corresponding pCT to the CBCT, and compared to that of the original plan. A replan was deemed needed for the CBCT anatomy once the recalculated dose distribution violated our dosimetric-based trigger criteria. A three-dimensional region of significant anatomical changes (region of interest, ROI) between each CBCT and the corresponding pCT was identified and 16 morphological features of the ROI were extracted. Additionally, eight features from the overlapped volume histograms (OVHs) of patient anatomy were extracted for each patient to characterize the patient specific anatomy. Based on the 24 extracted features and the evaluated replanning needs of the pCT-CBCT pairs, a nonlinear supporting vector machine was used to build a prediction model to identify the anatomical changes on CBCTs that would trigger plan adaptions. The most relevant features were selected using the sequential backward selection (SBS) algorithm and a shuffling-and-splitting validation scheme was used for model evaluation.

RESULTS

Fifty-Five CBCT-pCT pairs were identified of having a ROI, among which 21 CBCT anatomies required plan adaptions. For these 21 positive cases, statistically significant improvements in the sparing of lung, esophagus and spinal cord were achieved by plan adaptions. A high model performance of 0.929 AUC and 0.851 accuracy was achieved with six selected features including five ROI shape features and one OVH feature. Without involving the OVH features in the feature selection process, the mean AUC and accuracy of the model significantly decreased to 0.826 and 0.779, respectively. Further investigation showed that poor prediction performance with AUC of 0.76 was achieved by the univariate model in solving this binary classification task.

CONCLUSION

We built a prediction model based on the features of patient anatomy and the anatomical changes captured by on-treatment CBCT imaging to trigger plan adaption for lung cancer patients. This model effectively associated the anatomical changes with the dosimetric impacts for lung ART. This model can be a promising tool to assist the clinicians in making decisions for plan adaptions during the treatment courses. This article is protected by copyright. All rights reserved.

Quantifying the dosimetric effects of neck contour changes and setup errors on the spinal cord in patients with nasopharyngeal carcinoma: establishing a rapid estimation method

The purpose of this study was to quantify the effect of neck contour changes and setup errors on spinal cord (SC) doses during the treatment of nasopharyngeal carcinoma (NPC) and to establish a rapid dose estimation method. The setup errors and contour changes in 60 cone-beam computed tomography (CBCT) images of 10 NPC patients were analysed in different regions of the neck (C1-C3, C4-C5 and C6-C7). The actual delivered dose to the SC was calculated using the CBCT images, and univariate simulations were performed using the planning CT to evaluate the dose effects of each factor, and an index ${\mathrm{Dmax}}_{\mathrm{displaced}}$ was introduced to estimate the SC dose. Compared with the planned dose, the mean (maximum) Dmax increases in the C1-C3, C4-C5 and C6-C7 regions of the SC were 2.1% (12.3%), 1.8% (8.2%) and 2.5% (9.2%), respectively. The simulation results showed that the effects of setup error in the C1-C3, C4-C5 and C6-C7 regions were 1.5% (9.7%), 0.9% (8.2%) and 1.3% (6.3%), respectively, and the effects of contour change were 0.4% (1.7%), 0.7% (2.5%) and 1.5% (4.9%), respectively. The linear regression model can be used to estimate the dose effect of contour changes (R2 > 0.975) and setup errors (R2 = 0.989). Setup errors may lead to a significant increase in the SC dose in some patients. This study established a rapid dose estimation method, which is of great significance for the daily dose evaluation and the adaptive re-planning trigger of the SC.

Registration-guided deep learning image segmentation for cone beam CT-based online adaptive radiotherapy

PURPOSE

Adaptive radiotherapy (ART), especially online ART, effectively accounts for positioning errors and anatomical changes. One key component of online ART process is accurately and efficiently delineating organs at risk (OARs) and targets on online images, such as Cone Beam Computed Tomography (CBCT). Direct application of deep learning (DL)-based segmentation to CBCT images suffered from issues such as low image quality and limited available contour labels for training. To overcome these obstacles to online CBCT segmentation, we propose a registration-guided DL (RgDL) segmentation framework that integrates image registration algorithms and DL segmentation models.

METHODS

The RgDL framework is composed of two components: image registration and registration-guided DL segmentation. The image registration algorithm transforms / deforms planning contours, which were subsequently used as guidance by the DL model to obtain accurate final segmentations. We had two implementations of the proposed framework-Rig-RgDL (Rig for rigid body) and Def-RgDL (Def for deformable)-with rigid body (RB) registration or deformable image registration (DIR) as the registration algorithm, respectively, and U-Net as the DL model architecture. The two implementations of RgDL framework were trained and evaluated on seven OARs in an institutional clinical Head and Neck (HN) dataset.

RESULTS

Compared to the baseline approaches using the registration or the DL alone, RgDLs achieved more accurate segmentation, as measured by higher mean Dice similarity coefficients (DSC) and other distance-based metrics. Rig-RgDL achieved a DSC of 84.5% on seven OARs on average, higher than RB or DL alone by 4.5% and 4.7%. The average DSC of Def-RgDL was 86.5%, higher than DIR or DL alone by 2.4% and 6.7%. The inference time required by the DL model component to generate final segmentations of seven OARs was less than one second in RgDL. By examining the contours from RgDLs and DL case by case, we found that RgDL was less susceptible to image artifacts. We also studied how the performances of RgDL and DL vary with the size of the training dataset. The DSC of DL dropped by 12.1% as the number of training data decreased from 22 to 5, while RgDL only dropped by 3.4%.

CONCLUSION

By incorporating the patient-specific registration guidance to a population-based DL segmentation model, RgDL framework overcame the obstacles associated with online CBCT segmentation, including low image quality and insufficient training data, and achieved better segmentation accuracy than baseline methods. The resulting segmentation accuracy and efficiency show promise for applying this RgDL framework for online ART. This article is protected by copyright. All rights reserved.

DIR-based models to predict weekly anatomical changes in head and neck cancer proton therapy

Objective. We proposed two anatomical models for head and neck patients to predict anatomical changes during the course of radiotherapy.Approach. Deformable image registration was used to build two anatomical models: (1) the average model (AM) simulated systematic progressive changes across the patient cohort; (2) the refined individual model (RIM) used a patient's CT images acquired during treatment to update the prediction for each individual patient. Planning CTs and weekly CTs were used from 20 nasopharynx patients. This dataset included 15 training patients and 5 test patients. For each test patient, a spot scanning proton plan was created. Models were evaluated using CT number differences, contours, proton spot location deviations and dose distributions.Main results. If no model was used, the CT number difference between the planning CT and the repeat CT at week 6 of treatment was on average 128.9 Hounsfield Units (HU) over the test population. This can be reduced to 115.5 HU using the AM, and to 110.5 HU using the RIM3(RIM, updated at week (3). When the predicted contours from the models were used, the average mean surface distance of parotid glands can be reduced from 1.98 (no model) to 1.16 mm (AM) and 1.19 mm (RIM3) at week 6. Using the proton spot range, the average anatomical uncertainty over the test population reduced from 4.47 ± 1.23 (no model) to 2.41 ± 1.12 mm (AM), and 1.89 ± 0.96 mm (RIM3). Based on the gamma analysis, the average gamma index over the test patients was improved from 93.87 ± 2.48 % (no model) to 96.16 ± 1.84% (RIM3) at week 6.Significance. The AM and the RIM both demonstrated the ability to predict anatomical changes during the treatment. The RIM can gradually refine the prediction of anatomical changes based on the AM. The proton beam spots provided an accurate and effective way for uncertainty evaluation.

A two-step method to improve image quality of CBCT with phantom-based supervised and patient-based unsupervised learning strategies

Objective.In this study, we aimed to develop deep learning framework to improve cone-beam computed tomography (CBCT) image quality for adaptive radiation therapy (ART) applications.Approach.Paired CBCT and planning CT images of 2 pelvic phantoms and 91 patients (15 patients for testing) diagnosed with prostate cancer were included in this study. First, well-matched images of rigid phantoms were used to train a U-net, which is the supervised learning strategy to reduce serious artifacts. Second, the phantom-trained U-net generated intermediate CT images from the patient CBCT images. Finally, a cycle-consistent generative adversarial network (CycleGAN) was trained with intermediate CT images and deformed planning CT images, which is the unsupervised learning strategy to learn the style of the patient images for further improvement. When testing or applying the trained model on patient CBCT images, the intermediate CT images were generated from the original CBCT image by U-net, and then the synthetic CT images were generated by the generator of CycleGAN with intermediate CT images as input. The performance was compared with conventional methods (U-net/CycleGAN alone trained with patient images) on the test set.Results.The proposed two-step method effectively improved the CBCT image quality to the level of CT scans. It outperformed conventional methods for region-of-interest contouring and HU calibration, which are important to ART applications. Compared with the U-net alone, it maintained the structure of CBCT. Compared with CycleGAN alone, our method improved the accuracy of CT number and effectively reduced the artifacts, making it more helpful for identifying the clinical target volume.Significance.This novel two-step method improves CBCT image quality by combining phantom-based supervised and patient-based unsupervised learning strategies. It has immense potential to be integrated into the ART workflow to improve radiotherapy accuracy.

Onboard cone-beam CT-based replan evaluation for head and neck proton therapy

Purpose

Quality assurance computed tomography (QACT) is the current clinical practice in proton therapy to evaluate the needs for replan. QACT could falsely indicate replan because of setup issues that would be solved on the treatment machine. Deforming the treatment planning CT (TPCT) to the pretreatment CBCT may eliminate this issue. We investigated the performance of replan evaluation based on deformed TPCT (TPCTdir) for proton head and neck (H&N) therapy.

Methods and materials

Twenty‐eight H&N datasets along with pretreatment CBCT and QACT were used to validate the method. The changes in body volume were analyzed between the no‐replan and replan groups. The dose on the TPCTdir, the deformed QACT (QACTdir), and the QACT were calculated by applying the clinical plans to these image sets. Dosimetric parameters’ changes, including ΔD95, ΔDmean, and ΔD1 for the clinical target volumes (CTVs) were calculated. Receiver operating characteristic curves for replan evaluation based on ΔD95 on QACT and TPCTdir were calculated, using ΔD95 on QACTdir as the reference. A threshold for replan based on ΔD95 on TPCTdir is proposed. The specificities for the proposed method were calculated.

Results

The changes in the body contour were 95.8 ± 83.8 cc versus 305.0 ± 235.0 cc (p < 0.01) for the no‐replan and replan groups, respectively. The ΔD95, ΔDmean, and ΔD1 are all comparable for all the evaluations. The differences between TPCTdir and QACTdir evaluations were 0.30% ± 0.86%, 0.00 ± 0.22 Gy, and −0.17 ± 0.61 Gy for CTV ΔD95, ΔDmean, and ΔD1, respectively. The corresponding differences between the QACT and QACTdir were 0.12% ± 1.1%, 0.02 ± 0.32 Gy, and −0.01 ± 0.71 Gy. CTV ΔD95 > 2.6% in TPCTdir was chosen as the threshold to trigger QACT/replan. The corresponding specificity was 94% and 98% for the clinical practice and the proposed method, respectively.

Conclusions

The replan evaluation based on TPCTdir provides better specificity than that based on the QACT.

Comparison of an in-house hybrid DIR method to NiftyReg on CBCT and CT images for head and neck cancer

An in-house hybrid deformable image registration (DIR) method, which combines free-form deformation (FFD) and the viscous fluid registration method, is proposed. Its results on the planning computed tomography (CT) and the day 1 treatment cone-beam CT (CBCT) image from 68 head and neck cancer patients are compared with the results of NiftyReg, which uses B-spline FFD alone. Several similarity metrics, the target registration error (TRE) of annotated points, as well as the Dice similarity coefficient (DSC) and Hausdorff distance (HD) of the propagated organs at risk are employed to analyze their registration accuracy. According to quantitative analysis on mutual information, normalized cross-correlation, and the absolute pixel value differences, the results of the proposed DIR are more similar to the CBCT images than the NiftyReg results. Smaller TRE of the annotated points is observed in the proposed method, and the overall mean TRE for the proposed method and NiftyReg was 2.34 and 2.98 mm, respectively (p < 0.001). The mean DSC in the larynx, spinal cord, oral cavity, mandible, and parotid given by the proposed method ranged from 0.78 to 0.91, significantly higher than the NiftyReg results (ranging from 0.77 to 0.90), and the HD was significantly lower compared to NiftyReg. Furthermore, the proposed method did not suffer from unrealistic deformations as the NiftyReg did in the visual evaluation. Meanwhile, the execution time of the proposed method was much higher than NiftyReg (96.98 ± 11.88 s vs. 4.60 ± 0.49 s). In conclusion, the in-house hybrid method gave better accuracy and more stable performance than NiftyReg.

Adaptive proton therapy

Radiation therapy treatments are typically planned based on a single image set, assuming that the patient's anatomy and its position relative to the delivery system remains constant during the course of treatment. Similarly, the prescription dose assumes constant biological dose-response over the treatment course. However, variations can and do occur on multiple time scales. For treatment sites with significant intra-fractional motion, geometric changes happen over seconds or minutes, while biological considerations change over days or weeks. At an intermediate timescale, geometric changes occur between daily treatment fractions. Adaptive radiation therapy is applied to consider changes in patient anatomy during the course of fractionated treatment delivery. While traditionally adaptation has been done off-line with replanning based on new CT images, online treatment adaptation based on on-board imaging has gained momentum in recent years due to advanced imaging techniques combined with treatment delivery systems. Adaptation is particularly important in proton therapy where small changes in patient anatomy can lead to significant dose perturbations due to the dose conformality and finite range of proton beams. This review summarizes the current state-of-the-art of on-line adaptive proton therapy and identifies areas requiring further research.

Clinical suitability of deep learning based synthetic CTs for adaptive proton therapy of lung cancer

Purpose

Adaptive proton therapy (APT) of lung cancer patients requires frequent volumetric imaging of diagnostic quality. Cone‐beam CT (CBCT) can provide these daily images, but x‐ray scattering limits CBCT‐image quality and hampers dose calculation accuracy. The purpose of this study was to generate CBCT‐based synthetic CTs using a deep convolutional neural network (DCNN) and investigate image quality and clinical suitability for proton dose calculations in lung cancer patients.

Methods

A dataset of 33 thoracic cancer patients, containing CBCTs, same‐day repeat CTs (rCT), planning‐CTs (pCTs), and clinical proton treatment plans, was used to train and evaluate a DCNN with and without a pCT‐based correction method. Mean absolute error (MAE), mean error (ME), peak signal‐to‐noise ratio, and structural similarity were used to quantify image quality. The evaluation of clinical suitability was based on recalculation of clinical proton treatment plans. Gamma pass ratios, mean dose to target volumes and organs at risk, and normal tissue complication probabilities (NTCP) were calculated. Furthermore, proton radiography simulations were performed to assess the HU‐accuracy of sCTs in terms of range errors.

Results

On average, sCTs without correction resulted in a MAE of 34 ± 6 HU and ME of 4 ± 8 HU. The correction reduced the MAE to 31 ± 4HU (ME to 2 ± 4HU). Average 3%/3 mm gamma pass ratios increased from 93.7% to 96.8%, when the correction was applied. The patient specific correction reduced mean proton range errors from 1.5 to 1.1 mm. Relative mean target dose differences between sCTs and rCT were below ± 0.5% for all patients and both synthetic CTs (with/without correction). NTCP values showed high agreement between sCTs and rCT (<2%).

Conclusion

CBCT‐based sCTs can enable accurate proton dose calculations for APT of lung cancer patients. The patient specific correction method increased the image quality and dosimetric accuracy but had only a limited influence on clinically relevant parameters.

Can a ToF-PET photon attenuation reconstruction test stopping-power estimations in proton therapy? A phantom study

Objective. The aim of the phantom study was to validate and to improve the computed tomography (CT) images used for the dose computation in proton therapy. It was tested, if the joint reconstruction of activity and attenuation images of time-of-flight PET (ToF-PET) scans could improve the estimation of the proton stopping-power.Approach. The attenuation images, i.e. CT images with 511 keV gamma-rays (γCTs), were jointly reconstructed with activity maps from ToF-PET scans. Theβ⁺activity was produced with FDG and in a separate experiment with proton-induced radioactivation. The phantoms contained slabs of tissue substitutes. The use of theγCTs for the prediction of the beam stopping in proton therapy was based on a linear relationship between theγ-ray attenuation, the electron density, and the stopping-power of fast protons.Main results. The FDG based experiment showed sufficient linearity to detect a bias of bony tissue in the heuristic look-up table, which maps between x-ray CT images and proton stopping-power.γCTs can be used for dose computation, if the electron density of one type of tissue is provided as a scaling factor. A possible limitation is imposed by the spatial resolution, which is inferior by a factor of 2.5 compared to the one of the x-ray CT.γCTs can also be derived from off-line, ToF-PET scans subsequent to the application of a proton field with a hypofractionated dose level.Significance. γCTs are a viable tool to support the estimation of proton stopping with radiotracer-based ToF-PET data from diagnosis or staging. This could be of higher potential relevance in MRI-guided proton therapy.γCTs could form an alternative approach to make use of in-beam or off-line PET scans of proton-inducedβ⁺activity with possible clinical limitations due to the low number of coincidence counts.

Impact of CBCT frequency on target coverage and dose to the organs at risk in adjuvant breast cancer radiotherapy

The current study aims to assess the effect of cone beam computed tomography (CBCT) frequency during adjuvant breast cancer radiotherapy with simultaneous integrated boost (SIB) on target volume coverage and dose to the organs at risk (OAR). 50 breast cancer patients receiving either non-hypofractionated or hypofractionated radiotherapy after lumpectomy including a SIB to the tumor bed were selected for this study. All patients were treated in volumetric modulated arc therapy (VMAT) technique and underwent daily CBCT imaging. In order to estimate the delivered dose during the treatment, the applied fraction doses were recalculated on daily CBCT scans and accumulated using deformable image registration. Based on a total of 2440 dose recalculations, dose coverage in the clinical target volumes (CTV) and OAR was compared depending on the CBCT frequency. The estimated delivered dose (V95%) for breast-CTV and SIB-CTV was significantly lower than the planned dose distribution, irrespective of the CBCT-frequency. Between daily CBCT and CBCT on alternate days, no significant dose differences were found regarding V95% for both, breast-CTV and SIB-CTV. Dose distribution in the OAR was similar for both imaging protocols. Weekly CBCT though led to a significant decrease in dose coverage compared to daily CBCT and a small but significant dose increase in most OAR. Daily CBCT imaging might not be necessary to ensure adequate dose coverage in the target volumes while efficiently sparing the OAR during adjuvant breast cancer radiotherapy with SIB.

Intra-fractional dosimetric analysis of image-guided intracavitary brachytherapy of cervical cancer

Background

To assess the intra-fractional dosimetric variations of image-guided brachytherapy of cervical cancer.

Methods

A total of 38 fractions (9 patients) undergoing brachytherapy for cervical cancer underwent a CT scanning for treatment planning (planning CT) and a Cone-beam CT (CBCT) scanning immediately prior to delivery (pre-delivery CBCT). The variations of volumes as well as the dosimetric impact from treatment planning to delivery (intra-application) were evaluated. The dose volume histogram parameters including volume, D90 of high-risk clinical target volume (HRCTV) and D2cc of organs at risk (OARs) were recorded.

Results

The relative differences (mean ± 1SD) of the volume and D90 HRCTV across the two scans were − 2.0 ± 3.3% and − 1.2 ± 4.5%, respectively. The variations of D2cc for bladder, rectum, sigmoid and small intestine are − 0.6 ± 17.1%, 9.3 ± 14.6%, 7.2% ± 20.5% and 1.5 ± 12.6%, respectively. Most of them are statistically nonsignificant except the D2cc for rectum, which showed a significant increase (P = 0.001). Using 5% and 10% uncertainty of physical dose for HRCTV at a 6 Gy × 5 high-dose-rate schedule, the possibility of total equivalent doses in 2 Gy fractions (EQD2) lower than 85 Gy is close to 0% and 3%, respectively. Performing similar simulation at 15% and 20% uncertainty of a 4 Gy physical dose for OARs, the possibility of total EQD2 dose exceeding 75 Gy is about 70%. Less than 1% of the total EQD2 of OARs would exceed 80 Gy.

Conclusions

Average intra-fractional dosimetric variation of HRCTV was small in an interval of less than 1 h, and the possibility of total EQD2 exceeding 85 Gy is higher than 97%. The intra-fractional dosimetric variations of OARs might result in an overdose for OARs in a single fraction or the whole treatment. It is necessary to detect unfavorable anatomical changes by re-imaging and take interventions to minimize applied doses and reduce the risk of complications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-021-01870-x.

Deformable image registration to assist clinical decision for radiotherapy treatment adaptation for head and neck cancer patients

Head and neck (H&N) cancer patients often present anatomical and geometrical changes in tumors and organs at risk (OARs) during radiotherapy treatment. These changes may result in the need to adapt the existing treatment planning, using an expert's subjective opinion, for offline adaptive radiotherapy and a new treatment planning before each treatment, for online adaptive radiotherapy. In the present study, a fast methodology is proposed to assist in planning adaptation clinical decision using tumor and parotid glands percentage volume changes during treatment. The proposed approach was applied to 40 Η&Ν cases, with one planning Computed Tomography (pCT) image and CBCT scans for 6 weeks of treatment per case. Deformable registration was used for each patient's pCT image alignment to its weekly CBCT. The calculated transformations were used to align each patient's anatomical structures to the weekly anatomy. Clinical target volume (CTV) and parotid gland volume percentage changes were calculated in each case. The accuracy of the achieved image alignment was validated qualitatively and quantitatively. Furthermore, statistical analysis was performed to test if there is a statistically significant correlation between CTV and parotid glands volume percentage changes. Average MDA for CTV and parotid glands between corresponding structures defined by an expert in CBCTs and automatically calculated through registration was 1.4 ± 0.1 mm and 1.5 ± 0.1 mm, respectively. The mean registration time of the first CBCT image registration for 40 cases was lower than 3.4 min. Five patients show more than 20% tumor volume change. Six patients show more than 30% parotid glands volume change. Ten out of 40 patients proposed for planning adaptation. All the statistical tests performed showed no correlation between CTV/parotid glands percentage volume changes. The aim to assist in clinical decision making on a fast and automatic way was achieved using the proposed methodology, thereby reducing workload in clinical practice.